
import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase     :	List[Any]       			=  logging.get_logger(__name__)

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			ASTConfig()

  if "10-10" in model_name:
    pass
  elif "speech-commands" in model_name:
    __magic_name__								=			128
  elif "12-12" in model_name:
    __magic_name__								=			12
    __magic_name__								=			12
  elif "14-14" in model_name:
    __magic_name__								=			14
    __magic_name__								=			14
  elif "16-16" in model_name:
    __magic_name__								=			16
    __magic_name__								=			16
  else:
    raise ValueError("""Model not supported"""   )

  __magic_name__								=			"""huggingface/label-files"""
  if "speech-commands" in model_name:
    __magic_name__								=			35
    __magic_name__								=			"""speech-commands-v2-id2label.json"""
  else:
    __magic_name__								=			527
    __magic_name__								=			"""audioset-id2label.json"""

  __magic_name__								=			json.load(open(hf_hub_download(A_,	A_,	repo_type="""dataset"""   ),	"""r"""   )   )
  __magic_name__								=			{int(A_   ): v for k, v in idalabel.items()}
  __magic_name__								=			idalabel
  __magic_name__								=			{v: k for k, v in idalabel.items()}

  return config

def a__       (			A_   ):






  '''simple docstring'''





  if "module.v" in name:
    __magic_name__								=			name.replace("""module.v""",	"""audio_spectrogram_transformer"""   )
  if "cls_token" in name:
    __magic_name__								=			name.replace("""cls_token""",	"""embeddings.cls_token"""   )
  if "dist_token" in name:
    __magic_name__								=			name.replace("""dist_token""",	"""embeddings.distillation_token"""   )
  if "pos_embed" in name:
    __magic_name__								=			name.replace("""pos_embed""",	"""embeddings.position_embeddings"""   )
  if "patch_embed.proj" in name:
    __magic_name__								=			name.replace("""patch_embed.proj""",	"""embeddings.patch_embeddings.projection"""   )
  # transformer blocks
  if "blocks" in name:
    __magic_name__								=			name.replace("""blocks""",	"""encoder.layer"""   )
  if "attn.proj" in name:
    __magic_name__								=			name.replace("""attn.proj""",	"""attention.output.dense"""   )
  if "attn" in name:
    __magic_name__								=			name.replace("""attn""",	"""attention.self"""   )
  if "norm1" in name:
    __magic_name__								=			name.replace("""norm1""",	"""layernorm_before"""   )
  if "norm2" in name:
    __magic_name__								=			name.replace("""norm2""",	"""layernorm_after"""   )
  if "mlp.fc1" in name:
    __magic_name__								=			name.replace("""mlp.fc1""",	"""intermediate.dense"""   )
  if "mlp.fc2" in name:
    __magic_name__								=			name.replace("""mlp.fc2""",	"""output.dense"""   )
  # final layernorm
  if "audio_spectrogram_transformer.norm" in name:
    __magic_name__								=			name.replace("""audio_spectrogram_transformer.norm""",	"""audio_spectrogram_transformer.layernorm"""   )
  # classifier head
  if "module.mlp_head.0" in name:
    __magic_name__								=			name.replace("""module.mlp_head.0""",	"""classifier.layernorm"""   )
  if "module.mlp_head.1" in name:
    __magic_name__								=			name.replace("""module.mlp_head.1""",	"""classifier.dense"""   )

  return name

def a__       (			A_,	A_   ):






  '''simple docstring'''





  for key in orig_state_dict.copy().keys():
    __magic_name__								=			orig_state_dict.pop(A_   )

    if "qkv" in key:
      __magic_name__								=			key.split("""."""   )
      __magic_name__								=			int(key_split[3]   )
      __magic_name__								=			config.hidden_size
      if "weight" in key:
        __magic_name__								=			val[:dim, :]
        __magic_name__								=			val[dim : dim * 2, :]
        __magic_name__								=			val[-dim:, :]
      else:
        __magic_name__								=			val[:dim]
        __magic_name__								=			val[dim : dim * 2]
        __magic_name__								=			val[-dim:]
    else:
      __magic_name__								=			val

  return orig_state_dict

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			[
      """module.v.head.weight""",
      """module.v.head.bias""",
      """module.v.head_dist.weight""",
      """module.v.head_dist.bias""",
  ]
  for k in ignore_keys:
    state_dict.pop(A_,	A_   )

@torch.no_grad()
def a__       (			A_,	A_,	A_=False   ):






  '''simple docstring'''





  __magic_name__								=			get_audio_spectrogram_transformer_config(A_   )

  __magic_name__								=			{
      """ast-finetuned-audioset-10-10-0.4593""": (
          """https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
      ),
      """ast-finetuned-audioset-10-10-0.450""": (
          """https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
      ),
      """ast-finetuned-audioset-10-10-0.448""": (
          """https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
      ),
      """ast-finetuned-audioset-10-10-0.448-v2""": (
          """https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
      ),
      """ast-finetuned-audioset-12-12-0.447""": (
          """https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
      ),
      """ast-finetuned-audioset-14-14-0.443""": (
          """https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
      ),
      """ast-finetuned-audioset-16-16-0.442""": (
          """https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
      ),
      """ast-finetuned-speech-commands-v2""": (
          """https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
      ),
  }

  # load original state_dict
  __magic_name__								=			model_name_to_url[model_name]
  __magic_name__								=			torch.hub.load_state_dict_from_url(A_,	map_location="""cpu"""   )
  # remove some keys
  remove_keys(A_   )
  # rename some keys
  __magic_name__								=			convert_state_dict(A_,	A_   )

  # load 🤗 model
  __magic_name__								=			ASTForAudioClassification(A_   )
  model.eval()

  model.load_state_dict(A_   )

  # verify outputs on dummy input
  # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
  __magic_name__								=			-4.2677393 if """speech-commands""" not in model_name else -6.845978
  __magic_name__								=			4.5689974 if """speech-commands""" not in model_name else 5.5654526
  __magic_name__								=			1024 if """speech-commands""" not in model_name else 128
  __magic_name__								=			ASTFeatureExtractor(mean=A_,	std=A_,	max_length=A_   )

  if "speech-commands" in model_name:
    __magic_name__								=			load_dataset("""speech_commands""",	"""v0.02""",	split="""validation"""   )
    __magic_name__								=			dataset[0]["""audio"""]["""array"""]
  else:
    __magic_name__								=			hf_hub_download(
        repo_id="""nielsr/audio-spectogram-transformer-checkpoint""",	filename="""sample_audio.flac""",	repo_type="""dataset""",	)

    __magic_name__	,       __magic_name__								=			torchaudio.load(A_   )
    __magic_name__								=			waveform.squeeze().numpy()

  __magic_name__								=			feature_extractor(A_,	sampling_rate=16000,	return_tensors="""pt"""   )

  # forward pass
  __magic_name__								=			model(**A_   )
  __magic_name__								=			outputs.logits

  if model_name == "ast-finetuned-audioset-10-10-0.4593":
    __magic_name__								=			torch.tensor([-0.8760, -7.0042, -8.6602]   )
  elif model_name == "ast-finetuned-audioset-10-10-0.450":
    __magic_name__								=			torch.tensor([-1.1986, -7.0903, -8.2718]   )
  elif model_name == "ast-finetuned-audioset-10-10-0.448":
    __magic_name__								=			torch.tensor([-2.6128, -8.0080, -9.4344]   )
  elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
    __magic_name__								=			torch.tensor([-1.5080, -7.4534, -8.8917]   )
  elif model_name == "ast-finetuned-audioset-12-12-0.447":
    __magic_name__								=			torch.tensor([-0.5050, -6.5833, -8.0843]   )
  elif model_name == "ast-finetuned-audioset-14-14-0.443":
    __magic_name__								=			torch.tensor([-0.3826, -7.0336, -8.2413]   )
  elif model_name == "ast-finetuned-audioset-16-16-0.442":
    __magic_name__								=			torch.tensor([-1.2113, -6.9101, -8.3470]   )
  elif model_name == "ast-finetuned-speech-commands-v2":
    __magic_name__								=			torch.tensor([6.1589, -8.0566, -8.7984]   )
  else:
    raise ValueError("""Unknown model name"""   )
  if not torch.allclose(logits[0, :3],	A_,	atol=1e-4   ):
    raise ValueError("""Logits don't match"""   )
  print("""Looks ok!"""   )

  if pytorch_dump_folder_path is not None:
    Path(A_   ).mkdir(exist_ok=A_   )
    print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''   )
    model.save_pretrained(A_   )
    print(f'''Saving feature extractor to {pytorch_dump_folder_path}'''   )
    feature_extractor.save_pretrained(A_   )

  if push_to_hub:
    print("""Pushing model and feature extractor to the hub..."""   )
    model.push_to_hub(f'''MIT/{model_name}'''   )
    feature_extractor.push_to_hub(f'''MIT/{model_name}'''   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Optional[int]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='ast-finetuned-audioset-10-10-0.4593',
        type=str,
        help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    __lowerCAmelCase     :	Dict       			=  parser.parse_args()
    convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Tuple       			=  {'configuration_timm_backbone': ['TimmBackboneConfig']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	str       			=  ['TimmBackbone']


if TYPE_CHECKING:
    from .configuration_timm_backbone import TimmBackboneConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timm_backbone import TimmBackbone

else:
    import sys

    __lowerCAmelCase     :	str       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

